Showing papers by "Avinash C. Kak published in 2018"
TL;DR: A human-operated active learning framework is presented, rather than relying on previously collected fully labeled datasets for simulated experiments, that relies on multiple virtual machines working in parallel to carry out randomized scans in different portions of the geographic area in order to generate the active-learning based samples for human annotation.
5 citations
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TL;DR: In this paper, a two-stage cascaded approach is used to fine-tune the performance of a computer-vision-based automatic threat recognition (ATR) system.
Abstract: This work addresses the question whether it is possible to design a computer-vision based automatic threat recognition (ATR) system so that it can adapt to changing specifications of a threat without having to create a new ATR each time. The changes in threat specifications, which may be warranted by intelligence reports and world events, are typically regarding the physical characteristics of what constitutes a threat: its material composition, its shape, its method of concealment, etc. Here we present our design of an AATR system (Adaptive ATR) that can adapt to changing specifications in materials characterization (meaning density, as measured by its x-ray attenuation coefficient), its mass, and its thickness. Our design uses a two-stage cascaded approach, in which the first stage is characterized by a high recall rate over the entire range of possibilities for the threat parameters that are allowed to change. The purpose of the second stage is to then fine-tune the performance of the overall system for the current threat specifications. The computational effort for this fine-tuning for achieving a desired PD/PFA rate is far less than what it would take to create a new classifier with the same overall performance for the new set of threat specifications.
1 citations